Robust Robotic Arm Calibration combining Multi-Distance Optimization Approach with Lagrange Starfish Optimization Algorithm

In response to the limitations of existing robotic parameter calibration methods in terms of computational complexity, convergence speed, data requirements, and accuracy, this study proposes an innovative calibration scheme that combines an improved Lagrangian Starfish Optimization Algorithm (LSFA) with a Support Vector Machine (SVM) algorithm. By incorporating Lagrange interpolation and a multi-dimensional distance metric model (including Mahalanobis distance, Manhattan distance, Chebyshev distance, cosine distance, standardized Euclidean distance, and Euclidean distance), the enhanced starfish optimization algorithm significantly improves global search capabilities and local search accuracy. This effectively addresses issues such as initial value sensitivity, noise, and outliers, with the algorithm specifically designed for kinematic parameter calibration of robotic arms. Furthermore, the improved local search mechanism optimizes the position update strategy of starfish through a weighted system, preventing the algorithm from becoming trapped in local optima. To further enhance the accuracy of dynamic parameter calibration, this study integrates the SVM algorithm into the LSFA framework, proposing the LSFA-SVM method specifically for dynamic parameter calibration of robotic arms. Experiments demonstrate a 38.59% reduction in error compared to traditional SVM. The results indicate that LSFA excels in kinematic calibration of robotic arms, achieving a root mean square error (RMSE) of 0.29 mm, a 29.27% improvement over the traditional Starfish Optimization Algorithm (SFOA). This study provides an efficient and precise solution for robotic parameter calibration in complex environments.